
# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				UpperCamelCase_:				List[str]						=    {
				    """en""": """Machine learning is great, isn't it?""",
				    """ru""": """Машинное обучение - это здорово, не так ли?""",
				    """de""": """Maschinelles Lernen ist großartig, oder?""",
				}

				# BLUE scores as follows:
				# "pair": [fairseq, transformers]
				UpperCamelCase_:				Tuple						=    {
				    """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
				    """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
				    """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
				    """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
				}
				UpperCamelCase_:				int						=    F'''{src_lang}-{tgt_lang}'''

				UpperCamelCase_:				int						=    F'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
				os.makedirs(lowerCamelCase  ,     exist_ok=lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    os.path.join(lowerCamelCase  ,     """README.md"""    )
				print(F'''Generating {path}'''    )
				with open(lowerCamelCase  ,     """w"""  ,     encoding="""utf-8"""    ) as f:
								f.write(lowerCamelCase    )


# make sure we are under the root of the project
lowerCamelCase_    :			List[Any]    =	Path(__file__).resolve().parent.parent.parent
lowerCamelCase_    :			int    =	repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase_ ,							lowerCamelCase_ ,							lowerCamelCase_    :			Union[str, Any]    =	model_name.split("""-""")
							lowerCamelCase_    :			List[Any]    =	model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)








import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






